In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better‐informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross‐validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross‐validation methods exist; however, the influence of choosing a specific cross‐validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross‐validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross‐validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross‐validation methods, and the two cross‐validation methods yielded different projected present‐day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo‐Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross‐validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross‐validation methods in SDM studies.

Correlative species distribution models (SDMs) are important tools to estimate species’ geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species’ physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models’ sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.

As awareness of climate-related freshwater quality problems increases, more research is needed to better understand how climate impacts water quality. Climate has significant impacts on the mixing regime and nutrient status of lakes. However, the relationship between climatic variables, mixing regime, and nutrient status in large monomictic lakes requires further study. Here we applied a three-dimensional ecosystem model to the large monomictic Lake Biwa, where hypoxia had recurred over the past 60 years. The model was validated using monitoring data, and the relationships among mixing regime, nutrient status, and climatic variables have been investigated. The turnover timing, which represented the mixing regime, varied by 36 days and depended most on wind speed but least on air temperature. In the early period prior to waste-water treatment there was a strong linear relationship between phosphorus and deep-water dissolved oxygen (DO) concentrations. Following this period, the relationship weakened but remained significant. In addition, we find a weak relationship between turnover timing and deep-water DO concentrations. We suggest that later turnover timing may favor lower deep-water DO concentrations, which in turn may favor release of legacy nutrients from sediments. Although waste-water treatment has improved conditions in the lake, climate change induced alteration of turnover timing may adversely influence water quality. Maintaining water quality under continued warming may require more rigorous controls on phosphorus loading to the lake.

The nutrient load generated by excessive aquaculture farms leads to self-pollution around water, which destroys aqua-environment, and further leads to a decline in aquaculture production. The purpose of this study is to propose an index to assess the sustainability of inshore aquaculture in Kyushu area, considering nutrient loads from land and farms. The number and size of fish cages identified from Google satellite imagery are used to calculate annual fish production, which is then converted into annual loads of total nitrogen and total phosphorus. The pollutant load factor method is applied to calculate the land nutrient inflow. An index, including nutrient load from land and farms, bay area, water depth and distance from farms to bay, is proposed. The results show that for most of the cultured bays in Kyushu, the nutrient load from the farm is more than that from the land inflow. The bay with higher index value has a higher possibility of red tide occurrence and lower sustainability for aquaculture. Among which, location of fish farms, total nitrogen and total phosphorus loading are key factors impacting water quality within the bays.